AI-Powered Intelligent Conference Scheduling System: Optimizing Efficiency and Adaptability

Modern conference management involves complex scheduling challenges, including multi-track sessions, speaker availability, and participant preferences.This paper presents Con-fAI, an AI-driven scheduling system that integrates machine learning (ML), natural language processing (NLP), and reinforce-ment learning (RL) to optimize conference planning.The system utilizes BERT-based topic modeling for session categorization and Qlearning for efficient resource allocation, significantly reducing scheduling conflicts and improving schedule generation speed.Key features include a constraint satisfaction solver that dynamically adjusts schedules based on real-time changes and a predictive analytics engine for attendance forecasting, enhancing venue space utilization.The system employs federated learning for data privacy and has been successfully implemented in vari-ous conferences, demonstrating improved participant experience and organizational efficiency.Ethical safeguards, including bias mitigation and explainable AI, ensure fairness and transparency in decision-making.ConfAI establishes a scalable framework for AI-driven event management, addressing key challenges in collaborative scheduling.